​​Learning to Simulate Dynamic Environments with GameGAN
#Nvidia designed a GAN that able to recreate games without any game engine. To train it, authors of the model use experience collected by reinforcement learning and other techniques.
GameGAN successfully reconstructed all mechanics of #Pacman game. Moreover, the trained model can generate new mazes that have never appeared in the original game. It can even replace background (static objects) and foreground (dynamic objects) with different images!
As the authors say, applying reinforcement learning algorithms to real world tasks requires accurate simulation of that task. Currently designing such simulations is expensive and time-consuming. Using neural networks instead of hand-written simulations may help to solve these problems.
Paper: https://cdn.arstechnica.net/wp-content/uploads/2020/05/Nvidia_GameGAN_Research.pdf
Blog: https://blogs.nvidia.com/blog/2020/05/22/gamegan-research-pacman-anniversary/
Github Page: https://nv-tlabs.github.io/gameGAN/
#GAN #RL
#Nvidia designed a GAN that able to recreate games without any game engine. To train it, authors of the model use experience collected by reinforcement learning and other techniques.
GameGAN successfully reconstructed all mechanics of #Pacman game. Moreover, the trained model can generate new mazes that have never appeared in the original game. It can even replace background (static objects) and foreground (dynamic objects) with different images!
As the authors say, applying reinforcement learning algorithms to real world tasks requires accurate simulation of that task. Currently designing such simulations is expensive and time-consuming. Using neural networks instead of hand-written simulations may help to solve these problems.
Paper: https://cdn.arstechnica.net/wp-content/uploads/2020/05/Nvidia_GameGAN_Research.pdf
Blog: https://blogs.nvidia.com/blog/2020/05/22/gamegan-research-pacman-anniversary/
Github Page: https://nv-tlabs.github.io/gameGAN/
#GAN #RL
